It's sunny as you look out the airplane window, you check the weather at your destination and all looks great too, but the pilot says you're waiting for clearance from Air Traffic Control to take off. What gives?

Several factors limit the rate at which flights can land safely at an airport, including visibility, wind gusts and wind direction, cloud ceilings, on so on. The Air Traffic Control system in the United States manages these conditions through Ground Delay Programs (or GDPs). During a GDP, flights headed to an airport with reduced landing capacity are held on the ground at their origin airport (hence the term ground delay). This prevents flights from taking off too early and queuing in the air near the airport, which is more expensive and less safe.

I see you – leaning halfway out of your seat before the plane lands so you're ready to grab your bag and sprint to make your connecting flight – and I'm here to tell you it's going to be ok. Lumo is here to help.

At Lumo, we recognize that not all delays are created equal. A 30-minute delay on a nonstop flight may not matter much, but could be the difference between making your connection or not on a connecting itinerary.

I gave a brief talk at the Beat Live in New York in September on how to spot bad AI. It bugs me that the words AI and Machine Learning get thrown around so loosely and are rarely scrutinized, especially in travel. Also, while other industries are starting to have discussions around the ethics of machine learning and AI, travel's conversations around AI have stayed pretty superficial. A version of this was published in The Beat (subscription required).

When Google announced last year that they were releasing a flight delay prediction feature, we were thrilled! Being the only company that predicted delays was lonely, and we were excited that Google would bring some much-needed attention to a problem we feel strongly about.

While we can't give away all the details of our secret sauce, here's an outline of the basic ingredients. Our predictions are based on a combination of machine learning models that analyze vast amounts of historical and forecast data, and a super-fast simulation that is able to simulate the impact of projected constraints on all flights in the world rapidly (in a few minutes).